Bottom Line:
Sixteen HRV parameters, including time-domain, frequency-domain, and nonlinear parameters, were calculated from PPG captured by a smartphone for 30 healthy subjects and were compared with those derived from ECG.The results showed that M2D and TI algorithms had the best performance.These results suggest that the smartphone might be used for HRV measurement.

ABSTRACTHeart rate variability (HRV) is a useful clinical tool for autonomic function assessment and cardiovascular diseases diagnosis. It is traditionally calculated from a dedicated medical electrocardiograph (ECG). In this paper, we demonstrate that HRV can also be extracted from photoplethysmograms (PPG) obtained by the camera of a smartphone. Sixteen HRV parameters, including time-domain, frequency-domain, and nonlinear parameters, were calculated from PPG captured by a smartphone for 30 healthy subjects and were compared with those derived from ECG. The statistical results showed that 14 parameters (AVNN, SDNN, CV, RMSSD, SDSD, TP, VLF, LF, HF, LF/HF, nLF, nHF, SD1, and SD2) from PPG were highly correlated (r > 0.7, P < 0.001) with those from ECG, and 7 parameters (AVNN, TP, VLF, LF, HF, nLF, and nHF) from PPG were in good agreement with those from ECG within the acceptable limits. In addition, five different algorithms to detect the characteristic points of PPG wave were also investigated: peak point (PP), valley point (VP), maximum first derivative (M1D), maximum second derivative (M2D), and tangent intersection (TI). The results showed that M2D and TI algorithms had the best performance. These results suggest that the smartphone might be used for HRV measurement.

fig3: Comparison of HRV derived from the smartphone and the electrocardiograph for one subject. (a) R-to-R intervals (RRI) derived from the electrocardiogram. (b)–(f) Pulse-to-pulse intervals (PPI) derived from the smartphone photoplethysmogram, using the characteristic points determined by (b) peak point, (c) valley point, (d) maximum first derivative, (e) maximum second derivative, and (f) tangent intersection.

Mentions:
The ECG signals were first passed through a finite impulse response (FIR) low-pass filter with cutoff frequency of 11 Hz and then a FIR high-pass filter with cutoff frequency of 5 Hz to reduce most of the noise and interference [26]. Thereafter, they were resampled to 800 Hz with cubic spline interpolation to increase the temporal resolution. R-wave peak detection was performed using Pan and Tompkins' algorithm [26] and RRIs were obtained as the difference of successive R-wave peak locations. For both PPG and ECG signals, missed beats and false beats were manually identified and adjusted. An example of the obtained RRI and PPIs is shown in Figure 3.

fig3: Comparison of HRV derived from the smartphone and the electrocardiograph for one subject. (a) R-to-R intervals (RRI) derived from the electrocardiogram. (b)–(f) Pulse-to-pulse intervals (PPI) derived from the smartphone photoplethysmogram, using the characteristic points determined by (b) peak point, (c) valley point, (d) maximum first derivative, (e) maximum second derivative, and (f) tangent intersection.

Mentions:
The ECG signals were first passed through a finite impulse response (FIR) low-pass filter with cutoff frequency of 11 Hz and then a FIR high-pass filter with cutoff frequency of 5 Hz to reduce most of the noise and interference [26]. Thereafter, they were resampled to 800 Hz with cubic spline interpolation to increase the temporal resolution. R-wave peak detection was performed using Pan and Tompkins' algorithm [26] and RRIs were obtained as the difference of successive R-wave peak locations. For both PPG and ECG signals, missed beats and false beats were manually identified and adjusted. An example of the obtained RRI and PPIs is shown in Figure 3.

Bottom Line:
Sixteen HRV parameters, including time-domain, frequency-domain, and nonlinear parameters, were calculated from PPG captured by a smartphone for 30 healthy subjects and were compared with those derived from ECG.The results showed that M2D and TI algorithms had the best performance.These results suggest that the smartphone might be used for HRV measurement.

ABSTRACTHeart rate variability (HRV) is a useful clinical tool for autonomic function assessment and cardiovascular diseases diagnosis. It is traditionally calculated from a dedicated medical electrocardiograph (ECG). In this paper, we demonstrate that HRV can also be extracted from photoplethysmograms (PPG) obtained by the camera of a smartphone. Sixteen HRV parameters, including time-domain, frequency-domain, and nonlinear parameters, were calculated from PPG captured by a smartphone for 30 healthy subjects and were compared with those derived from ECG. The statistical results showed that 14 parameters (AVNN, SDNN, CV, RMSSD, SDSD, TP, VLF, LF, HF, LF/HF, nLF, nHF, SD1, and SD2) from PPG were highly correlated (r > 0.7, P < 0.001) with those from ECG, and 7 parameters (AVNN, TP, VLF, LF, HF, nLF, and nHF) from PPG were in good agreement with those from ECG within the acceptable limits. In addition, five different algorithms to detect the characteristic points of PPG wave were also investigated: peak point (PP), valley point (VP), maximum first derivative (M1D), maximum second derivative (M2D), and tangent intersection (TI). The results showed that M2D and TI algorithms had the best performance. These results suggest that the smartphone might be used for HRV measurement.